METHOD FOR SELECTING A NEOANTIGEN FOR DEVELOPING A PERSONALIZED ANTI-CANCER VACCINE

Claims

1. A method for selecting a tumor-specific immunogenic peptide obtained from a patient, comprising the steps of: (1) obtaining NGS sequence information from tumor cells or tumor tissues and normal cells or normal tissues obtained from a patient; (2) obtaining information on the sequences of tumor-specific peptides of at least 7 amino acids in length that have one or more amino acid changes in the NGS sequence of tumor cells or tumor tissues compared to the NGS sequence of normal cells or normal tissues; (3) measuring the expression level (TPM; transcripts per million) of a tumor-specific peptide or a gene encoding a tumor-specific peptide in tumor cells or tumor tissues; (4) application of tumor-specific peptide sequence information to a binding affinity prediction model and determination of the binding affinity (BA) index to the HLA molecule; (5) application of tumor-specific peptide sequence information to an immunogenicity prediction model and determination of an immunogenicity measure (IMM); (6) determining the priority of the tumor-associated peptide according to the following formula: Neoantigen priority score = min(TPM, 1) * (W1*BA+W2*IMM) / (W1+W2) [0 <W1<1, 0<W2<1]; и (7) selecting two or more tumor-specific immunogenic peptides based on a determined priority of tumor-specific peptides.

2. The method according to paragraph 1, further comprising the steps of: (a) obtaining information on the sequences of peptides obtained from tumor cells or tumor tissues and normal cells or normal tissues from a peptide database; (b) obtaining information on the sequences of HLA class I peptides from a biological sequence database; (c) constructing a binding affinity prediction model by training the data obtained in steps (a) and (b) on a model capable of predicting a binding affinity (BA) score for HLA, and obtaining a binding affinity score; and (d) constructing an immunogenicity prediction model by training the data obtained in steps (a) and (b) on a model capable of predicting the immunogenicity score (IMM) and obtaining the immunogenicity score.

3. The method according to claim 2, wherein the peptide database used to construct the prediction model in step (a) undergoes one or more selection steps from the following: i) removal of data that do not match HLA-A, HLA-B or HLA-C, ii) removal of data with mutations in HLA molecules, iii) removing data with peptide lengths of 7 or less or 15 or more, and iv) removal of amino acid sequences containing amino acid residues other than the 20 amino acids of human origin.

4. The method according to paragraphs 1-3, wherein the HLA comprises different types of HLA molecules or different HLA alleles.

5. The method according to any one of claims 1-3, wherein the learning in stages (c) and (d) is carried out by combining one or more artificial intelligence models and one or more learning methods.

6. The method according to any one of claims 1-3, wherein the binding affinity index is determined by an IC50 value or a Kd value related to binding to HLA.

7. The method according to any one of claims 1-3, wherein the immunogenicity index is determined based on one or more types of information selected from the group consisting of cytokine secretion by T cells, T cell proliferation due to immunogenic activation, chemokine secretion, and cytotoxicity of activated T cells when the candidate peptide is treated with immune cells or a sample containing immune cells.

8. The method according to claim 7, wherein the sample containing immune cells is selected from the group consisting of blood, leukocytes and peripheral blood mononuclear cells (PBMC), and the information is selected from at least two from the group consisting of IFN-g, IL-2, TNF-a CCL4, CXCL9 and granzyme B.

9. The method according to any one of claims 1-3, wherein the expression level of the candidate peptide or the gene encoding it in the sample obtained from the patient in step (3) is obtained by RNA sequencing from the patient sample data.

10. The method according to any one of claims 1-3, wherein the tumor-specific immunogenic peptide obtained from the patient is used to produce a personalized anti-cancer vaccine for the patient.

11. A composition of an anti-cancer vaccine comprising: a tumor-specific immunogenic peptide selected by the method according to any one of claims 1 to 3, or a nucleic acid molecule encoding the peptide; and pharmaceutically acceptable carrier.

12. A method for producing an anti-cancer vaccine, including a mixing step: a tumor-specific immunogenic peptide selected by the method according to any one of claims 1 to 3, or a nucleic acid molecule encoding the peptide; and pharmaceutically acceptable carrier.